BLO Stock Analysis
Summary
Backtest Summary - BLO
Generated: 2025-09-24 07:22:29
📊 Buy & Hold Benchmark
Total Return: +1036.36%
Analysis Period: Long-term (Multi-year)
Date Range: {‘start’: Timestamp(‘2011-06-27 00:00:00’), ’end’: Timestamp(‘2025-09-23 00:00:00’), ‘days’: 5202}
This represents the return from buying at the start and holding until the end of the analysis period.
Performance Overview
| Strategy | Symbol | Total Return | 3M Return | 6M Return | 12M Return | 24M Return | Excess Return | Sharpe Ratio | Max Drawdown | Trades | Win Rate | Final Value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| trend_momentum | BLO | 384.84% | -10.0% | -15.9% | -0.5% | -28.1% | -651.52% | 0.26 | -85.17% | 72 | 50.00% | $484,844 |
| dow_theory | BLO | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| volume_confirmation | BLO | 168.31% | -10.5% | -21.7% | -1.3% | -30.5% | -868.06% | 0.16 | -79.97% | 72 | 50.00% | $268,306 |
| bollinger_oscillators | BLO | 925.01% | 2.7% | 16.2% | 33.6% | 47.6% | -111.36% | 0.44 | -78.37% | 63 | 49.21% | $1,025,006 |
| macd_divergence | BLO | 0.00% | 0.0% | 0.0% | 0.0% | 0.0% | 0.00% | 0.00 | 0.00% | 0 | 0.00% | $100,000 |
| breakout_momentum | BLO | 514.37% | -9.2% | -17.0% | -5.6% | -18.1% | -521.99% | 0.30 | -65.05% | 48 | 50.00% | $614,371 |
| mean_reversion_multi_tf | BLO | 337.46% | 0.0% | 0.0% | 0.0% | 0.0% | -698.90% | 0.25 | -80.43% | 2 | 50.00% | $437,463 |
| relative_strength_rotation | BLO | 524.21% | -21.0% | -23.2% | -13.3% | -21.5% | -512.15% | 0.31 | -68.56% | 57 | 49.12% | $624,209 |
| gap_trading | BLO | 178.70% | -15.0% | -17.1% | 13.1% | 2.5% | -857.66% | 0.16 | -79.24% | 9 | 44.44% | $278,701 |
| volatility_expansion | BLO | 3129.06% | -15.0% | -17.1% | 4.8% | -5.0% | 2092.70% | 0.59 | -63.40% | 5 | 40.00% | $3,229,061 |
| momentum_kirkpatrick | BLO | 224.38% | -5.8% | -6.3% | 4.2% | -24.6% | -811.98% | 0.22 | -61.12% | 174 | 50.00% | $324,384 |
Best Strategy: volatility_expansion
- Symbol: BLO
- Total Return: 3129.06%
- Sharpe Ratio: 0.59
- Max Drawdown: -63.40%
- Final Portfolio Value: $3,229,061
Key Metrics
- Initial Capital: $100,000
- Analysis Date: 2025-09-24
- Portfolio Manager: Active (Extreme returns fix applied)
Period Analysis
This report includes period-based return analysis for the following timeframes:
- 3M Return: Performance over the last 3 months
- 6M Return: Performance over the last 6 months
- 12M Return: Performance over the last 12 months
- 24M Return: Performance over the last 24 months
Period-based analysis helps identify strategy behavior across different market conditions and time horizons.
Recent Trading Signals
📊 Today’s Signals (2025-09-24)
⚪ No new trading signals detected in today’s analysis.
📈 Most Recent Signals by Strategy
🔴 Trend Momentum: Last SELL on 2025-09-23
- 📊 Total Confidence: 19.6%
- 🟢 Composite: 22.2%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 18.7%
- 🟡 Institutional: 27.6%
- 🟣 Quantitative: 29.7%
🔴 Volume Confirmation: Last SELL on 2025-09-23
- 📊 Total Confidence: 70.6%
- 🟢 Composite: 68.3%
- 🔵 Conservative: 1.6%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 85.2%
- 🟣 Quantitative: 98.1%
🟢 Bollinger Oscillators: Last BUY on 2025-09-10
- 📊 Total Confidence: 24.6%
- 🟢 Composite: 28.1%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 24.4%
- 🟡 Institutional: 33.9%
- 🟣 Quantitative: 36.6%
🔴 Breakout Momentum: Last SELL on 2025-09-08
- 📊 Total Confidence: 19.4%
- 🟢 Composite: 23.6%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 20.1%
- 🟡 Institutional: 24.3%
- 🟣 Quantitative: 29.1%
🔴 Mean Reversion Multi Tf: Last SELL on 2019-10-17
- 📊 Total Confidence: 15.8%
- 🟢 Composite: 21.8%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 18.3%
- 🟡 Institutional: 22.2%
- 🟣 Quantitative: 16.6%
🟢 Relative Strength Rotation: Last BUY on 2025-07-22
- 📊 Total Confidence: 20.7%
- 🟢 Composite: 23.6%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 20.1%
- 🟡 Institutional: 29.3%
- 🟣 Quantitative: 30.6%
🟢 Gap Trading: Last BUY on 2022-10-18
- 📊 Total Confidence: 67.3%
- 🟢 Composite: 66.6%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 100.0%
- 🟡 Institutional: 80.8%
- 🟣 Quantitative: 89.0%
🟢 Volatility Expansion: Last BUY on 2024-10-09
- 📊 Total Confidence: 32.1%
- 🟢 Composite: 39.5%
- 🔵 Conservative: 1.3%
- 🔴 Aggressive: 37.7%
- 🟡 Institutional: 41.8%
- 🟣 Quantitative: 40.2%
🔴 Momentum Kirkpatrick: Last SELL on 2025-09-18
- 📊 Total Confidence: 21.2%
- 🟢 Composite: 20.5%
- 🔵 Conservative: 0.0%
- 🔴 Aggressive: 17.1%
- 🟡 Institutional: 31.0%
- 🟣 Quantitative: 37.2%
📊 How Confidence Is Calculated
Confidence percentages tell you how much to trust a trading signal based on the strategy’s historical performance.
🎯 Current Method: Composite (Balanced)
- Sharpe Ratio: Up to 20 points (risk-adjusted returns)
- Win Rate: Up to 30 points (percentage of profitable trades)
- Total Return: Up to 50 points (overall profitability)
📈 Available Confidence Methods:
- 🟢 Composite (Balanced): Current method - balanced approach for most traders
- 🔵 Conservative (Risk-Averse): Emphasizes safety and downside protection
- 🔴 Aggressive (Growth-Focused): Prioritizes high returns over risk
- 🟡 Institutional (Modern Portfolio Theory): Professional fund management approach
- 🟣 Quantitative (Statistical): Mathematical and statistical measures
🎯 Confidence Levels:
- 70%+: Strong performer - trust this signal more
- 50-70%: Decent performer - moderate trust
- 30-50%: Weak performer - be cautious
- <30%: Poor performer - low trust
💡 Signal Interpretation
- 🟢 BUY signals: Suggest potential upward price movement
- 🔴 SELL signals: Suggest potential downward price movement
- ⚪ HOLD signals: Suggest maintaining current position
- 📊 Confidence: Higher percentages indicate stronger signal conviction
- 🎯 CONSENSUS: Overall recommendation based on multiple strategy agreement
📚 Detailed Confidence Method Explanations
🟢 Composite (Balanced) - Current Method
Formula: (Sharpe×20) + (WinRate×30) + (Return×50)
Used by: Individual traders, retail investors
Why: Balanced approach that considers risk, consistency, and returns equally. Good for most trading styles.
Example: Strategy with 0.4 Sharpe, 60% win rate, 80% return = (0.4×20) + (0.6×30) + (0.8×50) = 66% confidence
🔵 Conservative (Risk-Averse)
Formula: (Sharpe×25) + (WinRate×35) + (Return×40) - DrawdownPenalty + SafetyBonus
Used by: Pension funds, insurance companies, risk-averse investors
Why: Prioritizes capital preservation over growth. Heavily penalizes strategies with large drawdowns.
Key Features:
- Higher weight on consistency (win rate)
- Penalty for drawdowns >5%
- Bonus for low-risk strategies
- Caps returns at 50% to avoid overvaluing risky strategies
🔴 Aggressive (Growth-Focused)
Formula: (Return×60) + (Sharpe×15) + (WinRate×25) + HighReturnBonus
Used by: Hedge funds, growth investors, aggressive traders
Why: Maximizes returns regardless of risk. Suitable for investors who can tolerate volatility.
Key Features:
- 60% weight on raw returns
- Lower weight on risk adjustment
- Bonus for strategies with >50% returns
- Allows returns up to 200% contribution
🟡 Institutional (Modern Portfolio Theory)
Formula: InfoRatio + Consistency + RiskAdjustedReturn + ReturnComponent + SignificanceBonus
Used by: Mutual funds, pension funds, institutional investors
Why: Based on academic finance theory and institutional requirements. Emphasizes statistical significance.
Key Features:
- Information ratio (like Sharpe but more robust)
- Return-to-drawdown ratio
- Bonus for statistically significant results (>100 trades)
- Follows modern portfolio theory principles
🟣 Quantitative (Statistical)
Formula: CalmarRatio + SterlingRatio + WinRate + Return + SampleSize + StatisticalSignificance
Used by: Quantitative funds, algorithmic trading systems, research institutions
Why: Uses advanced statistical measures and mathematical optimization. Most rigorous approach.
Key Features:
- Calmar ratio (return/max drawdown)
- Sterling ratio (similar to Calmar)
- Sample size adjustment for statistical validity
- T-statistic proxy for significance testing
- Mathematical optimization of weights
🏛️ Financial Industry Context
Goldman Sachs: Uses similar multi-factor scoring for strategy selection
Renaissance Technologies: Employs statistical significance testing like our Quantitative method
Bridgewater: Emphasizes risk parity similar to our Conservative approach
AQR: Uses academic factors like our Institutional method
Two Sigma: Applies quantitative methods similar to our Statistical approach
